In digital signal processing, data compression has become extremely important in the transmission of data. The process of reducing the size of a data file is referred to as data compression. Compression is useful because it helps reduce resource usage, such as data storage space or transmission capacity. However, once the data is compressed, it has to then become decompressed for it to become usable. Because compressed data must be decompressed to use, this extra processing imposes computational or other costs through decompression. In addition, the design of data compression schemes involves trade-offs among various factors, including the degree of compression, the amount of distortion introduced (when using lossy data compression), and the computational resources required to compress and decompress the data. For instance, a compression scheme for video may require expensive hardware for the video to be decompressed fast enough to be viewed as it is being decompressed. The option to decompress the video in full before watching it may be inconvenient or require additional storage.
In video compression, this trade off can become a very important consideration because uncompressed video requires a very high data rate. Most video compression algorithms combine spatial image compression and temporal motion compensation. In addition, video compression streams also accompany audio streams in one package. Therefore, in the design of most video coding standards, the primary goal is aimed at having the highest coding efficiency. Coding efficiency is the ability to encode video at the lowest possible bit rate while maintaining a certain level of video quality. The tradeoff between the video quality and bit rate has posed many practical challenges in recent years.
High Efficiency Video Coding (hereinafter HEVC) is a video compression standard, that has gained popularity. The reason for this popularity is that HEVC can double the data compression ratio compared at the same level of video quality. HEVC supports a number of techniques such as color spacing, scalable coding extensions, and multi-view extensions. Moreover, the HEVC video compression standard specifies, among others, a video decoding process that operates according to a so-called conformance point. The conformance point corresponds to the point, in a video decoding and rendering process, where the conformance of a decoded video sequence with the HEVC standard can be checked. It corresponds to the fixed point in number output of the video decoder, before any subsequent operations apply to the decoded picture components. However, using these capabilities simultaneously have not been possible in most cases. Therefore, a technique is desirable that can take full advantage of some of these capabilities simultaneously.
Other techniques have been proposed to encode and decode picture/video, especially high-dynamic range picture/video.
FIG. 1 is a block diagram depicting a conventional high dynamic range (hereinafter HDR) decoding workflow. In most HDR imaging techniques, imaging and photography is used to reproduce a greater dynamic range of luminosity than is possible with standard digital imaging or photographic techniques. The technique produces superior images that have to be maintained during video and image transmissions. To this end, as shown in FIG. 1, a general HDR reconstruction system (HDR reconstruction 12, color up-sampling 13, HDR post-processing 14) is disposed after a legacy (HEVC) video decoder 11, which aims at reconstructing HDR content from the decoded video sequence produced by the HEVC decoder. In most video distribution use cases, the HEVC coded sequences are represented in 4:2:0 chroma format, and component samples are represented by 10 bits fixed point numbers.
HDR images can be computer renderings and images resulting from merging multiple low-dynamic-range (LDR) or standard-dynamic-range (SDR) photographs. In addition, HDR images can also be acquired using special image sensors, like an oversampled binary image sensor. In the context of the distribution of a compressed HDR video, when also distributing an associated SDR video representative of the HDR with a more limited dynamic range simultaneously, there are several challenges. These challenges are aggravated in cases where there are no SDR associated video and, as a consequence, the generation of the SDR video is also part of the problem to be resolved.
Referring back to FIG. 1, a decoder that can be used with HEVC coded sequences, and one that especially uses HDR imaging, must have a profile that allows for a bit depth of 8-bits to 10-bits per sample with 4:2:0 chroma sampling. Such HEVC decoders must be capable of decoding bitstream made with a higher bit depth that allows for a greater number of colors and accommodate a higher bit depth to allow for a smoother transition of color which minimizes the problem of color banding. In the example used in FIG. 1, a large part of the HDR reconstruction process is performed in 4:4:4 format, whereas the color format classically used in the HEVC coding/decoding process is 4:2:0. The result is high computational complexity of the HDR reconstruction process.
Moreover, the HEVC video compression standard specifies, among others, a video decoding process that operates according to a so-called conformance point. The conformance point corresponds to the point, in a video decoding and rendering process, where the conformance of a decoded video sequence with the HEVC standard can be checked. It corresponds to the fixed point number output of the video decoder, before any subsequent operations apply to the decoded picture components (like chroma up-sampling, color space conversion and/or video signal adaptation to the output display). A conformance point that may be considered by the Moving Picture Expert Group (hereinafter MPEG) standardization body for MPEG/HDR video compression is located just before the 4:2:0 to 4:4:4 chroma up-sampling.
In addition, European Patent Application no 15290214.4 filed on Aug. 24, 2015 discloses picture/video encoding and decoding techniques, enabling the encoding and decoding of high-dynamic range picture/video. Such techniques rely, on the encoder side, on mapping, for example, an HDR picture onto a SDR picture represented in a format compatible with the legacy SDR workflow. Exemplary, the format may be the 8-bit YUV format dedicated to High Definition TV (as defined by the standard ITU-R Rec BT.709) or the 10-bit YUV format dedicated to Ultra High Definition TV (as defined by the standard ITU-R Rec BT.2020). It further comprises encoding the obtained SDR picture by using a legacy SDR image coder. For instance, the coder may be the standard 8-bit h264/AVC main profile or the standard 10-bit HEVC main profile of, e.g., HEVC (or any other codec workable by the workflow). Further, the distribution scheme comprises distributing the bit-stream of the obtained encoded SDR picture. On the decoder side, two scenarios are possible depending on the addressed user. In a first scenario, a decoded SDR picture is obtained from the distributed bit-stream and is displayed on a SDR-capable device. In a second scenario, a decoded HDR picture is obtained from the distributed bit-stream by first obtaining a decoded SDR picture and by second applying a mapping from the decoded SDR picture to the decoded HDR picture.
According to this technique implementing a HEVC decoder, most of SDR-to-HDR mapping process is applied on the decoded color pictures with a 4:4:4 chroma format, due to the use of the XYZ color space, not adapted to perform 4:2:0 to 4:4:4 up-sampling at the beginning on the mapping process. The result is a high computational complexity decoder.
It would thus be desirable to have an HDR decoding process where most of the process is performed in 4:2:0 domain. Moreover, it would be desirable to have an HDR decoding process where the output of the decoding is represented with 10-bit integer samples, so that the HDR decoding process produces an HDR signal that conforms to an established standard HDR video signal.